Recent extreme weather in the UK highlights the need to understand the potential for more extreme events in the present-day, and how such events may change with global warming. We present a methodology for more efficiently sampling extremes in future climate projections. As a proof-of-concept, we use the UK’s most recent set of national Climate Projections (UKCP18). UKCP18 includes a 15-member perturbed parameter ensemble (PPE) of coupled global simulations, providing a range of climate projections incorporating uncertainty in both internal variability and forced response. However, this ensemble is too small to adequately sample extremes with return periods over 100 years, which are of interest to policy-makers and adaptation planners. To better understand the statistics of these events, we use distributed computing to run three ~1000-member initial-condition ensembles with the atmosphere-only HadAM4 model at 60km resolution on volunteers’ computers, taking boundary conditions from future extreme winters within the UKCP18 ensemble. We find that every UKCP18 extreme winter is captured within our ensembles, and that two of the three ensembles are conditioned towards producing extremes by the boundary conditions. Our ensembles contain several extremes that would only be expected to be sampled by a UKCP18 PPE of over 500 members, which would be prohibitively expensive with current supercomputing resource. The most extreme winters simulated lie above those for UKCP18 by 0.85K for daily maximum temperature and 37% of the present-day average for precipitation (UK winter means).

The simultaneous occurrence of extremely wet winters at multiple locations in the same region can contribute to widespread flooding and associated socio-economic losses. However, the change in the spatial extent of precipitation extremes is largely overlooked in climate change assessments. Employing new multi-thousand-year climate model simulations, we show that under both 2.0°C and 1.5°C warming scenarios, wintertime precipitation extreme extents would increase over about 80-90% of the Northern Hemisphere. Stabilising at 1.5°C rather than 2°C would reduce the average magnitude of the increase by 1.6-2 times. According to the climate model, the increased extents are caused by increases in precipitation intensity increasing rather than changes in the spatial organisation of the events. Relatively small percentage increases in precipitation intensities (e.g., by 4%) can drive disproportionately larger, by 1-2 orders of magnitude, growth in the spatial extents (by 97%).